Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks

TL;DR

This paper introduces the concept of universality in module transferability for single image super-resolution, proposes a metric to quantify it, and designs optimized modules that improve performance and efficiency across various datasets.

Contribution

The paper defines universality for module transferability, proposes the UAE metric, and designs Cycle Residual Blocks that enhance super-resolution models' performance and efficiency.

Findings

Modules with higher universality improve transferability.

Optimized modules achieve up to 0.83 dB PSNR gain.

Parameter reduction of 71.3% with negligible quality loss.

Abstract

Deep learning has substantially advanced the field of Single Image Super-Resolution (SISR). However, existing research has predominantly focused on raw performance gains, with little attention paid to quantifying the transferability of architectural components. In this paper, we introduce the concept of "Universality" and its associated definitions, which extend the traditional notion of "Generalization" to encompass the ease of transferability of modules. We then propose the Universality Assessment Equation (UAE), a metric that quantifies how readily a given module can be transplanted across models and reveals the combined influence of multiple existing metrics on transferability. Guided by the UAE results of standard residual blocks and other plug-and-play modules, we further design two optimized modules: the Cycle Residual Block (CRB) and the Depth-Wise Cycle Residual Block (DCRB). Through comprehensive experiments on natural-scene benchmarks, remote-sensing datasets, and other low-level tasks, we demonstrate that networks embedded with the proposed plug-and-play modules outperform several state-of-the-art methods, achieving a PSNR improvement of up to 0.83 dB or enabling a 71.3% reduction in parameters with negligible loss in reconstruction fidelity. Similar optimization approaches could be applied to a broader range of basic modules, offering a new paradigm for the design of plug-and-play modules.